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summary-cenagro-code.R
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summary-cenagro-code.R
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##################################
# IV CENSO NACIONAL AGROPECUARIO 2012 - EJEMPLOS DE PREGUNTAS
# INPUT : CENAGRO database
# OUTPUT : Informacion socioeconomica de productores de papa
# AUTOR: Marvin Quispe Sedano
###############################################################
# Nota: Tildes omitidas en todos los textos
###############################################################
# Librerias
pkgs = c("data.table", "cluster", "dplyr", "ggplot2",
"Rtsne", "raster", "ggsn", "partykit", "factoextra",
"Hmisc", "corrgram", "openxlsx")
# install.packages(pkgs)
lapply(pkgs, library, character.only = TRUE)
###############################################################
# Configurar el directorio de trabajo
setwd("C:/Users/Asus/Documents/R/lateblight_tests/cenagro")
wd_data <- list.files("C:/Users/Asus/Documents/R/lateblight_tests/cenagro/combined/",
pattern=".rds", full=TRUE)
data_list <- lapply(wd_data, readRDS)
###############################################################
# 1.- UNIDADES AGROPECUARIAS QUE SIEMBRAN NATIVA Y MEJORADA
# Cargamos los codigos de cultivo y eliminamos vacios
df_codigos <- setDT(data_list[[3]])
df_codigos <- df_codigos[!(is.na(df_codigos$P024_03) | df_codigos$P024_03==""), ]
# Unidades agropecuarias que siembran nativa y mejorada
# Codigos : 2612: blanca; 2610: amarga; 2611: amarilla; 2613: color; 2614: huayro; 2615: nativa
df_nym_total <- df_codigos[ , if(any(P024_03 == 2612) & (any(P024_03 == 2610) | any(P024_03 == 2611) | any(P024_03 == 2613) | any(P024_03 == 2614) | any(P024_03 == 2615))) .SD,
by = list(P001, P002, P003, P007X, P008, NPRIN)]
# Número de parcelas por unidad agropecuaria
df_parcelas_nym_total <- df_nym_total[ , max(NPARCX) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)]
# Seleccionar solo las parcelas que contengan alguna variedad de papa
df_nym_papa <- df_nym_total[P024_03 %in% c(2610, 2611, 2612, 2613, 2614, 2615) ]
# Número de parcelas por unidad agropecuaria que solo tienen papa
df_parcelas_nym_papa <- df_nym_papa[ , length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)]
# Numero de unidades agropecuarias que siembran nativa y mejorada
nrow(df_parcelas_nym_total)
nrow(df_parcelas_nym_papa)
# Superficie sembrada del cultivo en hectareas
sum(df_nym_papa$P025)
# Superficie que se encuentra bajo riego y secano
df_nym_papa[ P026 == "Riego", sum(P025)]
df_nym_papa[ P026 == "Secano", sum(P025)]
# Superficie segun destino de la produccion
df_nym_papa[ P028 == "Autoconsumo", sum(P025)]
df_nym_papa[ P028 == "Autoinsumo", sum(P025)]
df_nym_papa[ P028 == "Alimento para sus animales", sum(P025)]
df_nym_papa[ P028 == "Venta", sum(P025)]
# Superficie segun destino de venta
df_nym_papa[ P029_01 == "Mercado nacional", sum(P025)]
df_nym_papa[ P029_02 == "Mercado exterior", sum(P025)]
df_nym_papa[ P029_03 == "Agroindustria", sum(P025)]
# Uniendo otras bases de datos de interes
df_nym_papa <- merge(df_nym_papa,
data_list[[4]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN", "NPARCX"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN", "NPARCY"),
all.x = TRUE)
# UA segun regimen de pertenencia de las parcelas
nrow(df_nym_papa[P037_01_03 == "Con título inscrito en registros públicos",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P037_01_03 == "Con título no inscrito en registros públicos",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P037_01_03 == "Sin título, pero en trámite de título",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P037_01_03 == "Sin título, ni trámite",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA segun pertenencia a una comunidad campesina o nativa
nrow(df_nym_papa[P039_01 == "Comunidad campesina",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P039_01 == "Comunidad nativa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Superficie sembrada del cultivo segun pertenencia a una comunidad
df_nym_papa[ P039_01 == "Comunidad campesina", sum(P025)]
df_nym_papa[ P039_01 == "Comunidad nativa", sum(P025)]
# Uniendo otras bases de datos de interes
df_nym_papa <- merge(df_nym_papa,
data_list[[7]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN"),
all.x = TRUE)
# UA que usan semilla y/o plantones certificados
nrow(df_nym_papa[P051 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican guano, estiercol u abono organico
nrow(df_nym_papa[P052 == "En cantidad suficiente" | P052 == "En poca cantidad",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican fertilizantes quimicos
nrow(df_nym_papa[P053 == "En cantidad suficiente" | P053 == "En poca cantidad",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican insecticidas quimicos
nrow(df_nym_papa[P054_01 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican insecticidas no quimicos o biologico
nrow(df_nym_papa[P054_02 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican herbicida
nrow(df_nym_papa[P054_03 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican fungicida
nrow(df_nym_papa[P054_04 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican control biologico
nrow(df_nym_papa[P056 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Uniendo otras bases de datos de interes
data_list[[10]] <- data_list[[10]][data_list[[10]]$P110 == "Productor(a)",]
df_nym_papa <- merge(df_nym_papa,
data_list[[10]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN"),
all.x = TRUE)
# UA segun sexo del productor
nrow(df_nym_papa[P111 == "Hombre",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P111 == "Mujer",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Edad promedio del productor
df_nym_papa[ , mean(P112, na.rm = T)]
# UA segun nivel de educacion del productor
nrow(df_nym_papa[P114 == "Sin nivel",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P114 == "Inicial",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P114 == "Primaria incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P114 == "Primaria completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P114 == "Secundaria incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P114 == "Secundaria completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P114 == "Superior no univ. incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P114 == "Superior no univ. completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P114 == "Superior univ. incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P114 == "Superior univ. completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA segun lengua de origen del productor
nrow(df_nym_papa[P115 == "Quechua",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P115 == "Aymara",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P115 == "Ashaninka",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P115 == "Otra lengua nativa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P115 == "Castellano",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P115 == "Idioma extranjero",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Uniendo otras bases de datos de interes
df_nym_papa <- df_nym_papa[,-c("TIPO_REC.x", "TIPO_REC.y")]
df_nym_papa <- merge(df_nym_papa,
data_list[[11]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN"),
all.x = TRUE)
# UA segun el tipo de saneamiento y alcantarillado
nrow(df_nym_papa[P118 == "Red pública de desagüe dentro de la vivienda",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P118 == "Red pública de desagüe, fuera de la vivienda pero dentro de la edificación",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P118 == "Pozo séptico",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P118 == "Pozo ciego o negro / letrina",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P118 == "Río, acequia o canal",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P118 == "Otro",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P118 == "No tiene",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que cuentan con cocina mejorada
nrow(df_nym_papa[P119 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que cuentan con computadora
nrow(df_nym_papa[P120_01 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que cuentan con internet
nrow(df_nym_papa[P120_02 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA segun lejania o cercania a la capital distrital
nrow(df_nym_papa[P121_01 == "Vive en la capital distrital",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_nym_papa[P121_01 == "Más de 24 horas",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Guardar base de datos final agregando la base de datos de referencia espacial
df_nym_papa <- merge(df_nym_papa,
data_list[[1]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN"),
all.x = TRUE)
write.csv(df_nym_papa, "data-nym-cenagro.csv", row.names = F)
###############################################################
# 2.- UNIDADES AGROPECUARIAS QUE SIEMBRAN SOLO MEJORADA
df_nym_total_2 <- df_codigos[ , if(!(any(P024_03 == 2612) & (any(P024_03 == 2610) | any(P024_03 == 2611) | any(P024_03 == 2613) | any(P024_03 == 2614) | any(P024_03 == 2615)))) .SD,
by = list(P001, P002, P003, P007X, P008, NPRIN)]
# Seleccionar solo las parcelas que contengan papa mejorada
df_m_papa <- df_nym_total_2[P024_03 %in% c(2612) ]
# Número de parcelas por unidad agropecuaria que solo tienen papa mejorada
df_parcelas_m_papa <- df_m_papa[ , max(NPARCX) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)]
# Numero de unidades agropecuarias que siembran mejorada
nrow(df_parcelas_m_papa)
# Superficie sembrada del cultivo en hectareas
sum(df_m_papa$P025)
# Superficie que se encuentra bajo riego y secano
df_m_papa[ P026 == "Riego", sum(P025)]
df_m_papa[ P026 == "Secano", sum(P025)]
# Superficie segun destino de la produccion
df_m_papa[ P028 == "Autoconsumo", sum(P025)]
df_m_papa[ P028 == "Autoinsumo", sum(P025)]
df_m_papa[ P028 == "Alimento para sus animales", sum(P025)]
df_m_papa[ P028 == "Venta", sum(P025)]
# Superficie segun destino de venta
df_m_papa[ P029_01 == "Mercado nacional", sum(P025)]
df_m_papa[ P029_02 == "Mercado exterior", sum(P025)]
df_m_papa[ P029_03 == "Agroindustria", sum(P025)]
# Uniendo otras bases de datos de interes
df_m_papa <- merge(df_m_papa,
data_list[[4]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN", "NPARCX"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN", "NPARCY"),
all.x = TRUE)
# UA segun regimen de pertenencia de las parcelas
nrow(df_m_papa[P037_01_03 == "Con título inscrito en registros públicos",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P037_01_03 == "Con título no inscrito en registros públicos",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P037_01_03 == "Sin título, pero en trámite de título",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P037_01_03 == "Sin título, ni trámite",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA segun pertenencia a una comunidad campesina o nativa
nrow(df_m_papa[P039_01 == "Comunidad campesina",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P039_01 == "Comunidad nativa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Superficie sembrada del cultivo segun pertenencia a una comunidad
df_m_papa[ P039_01 == "Comunidad campesina", sum(P025)]
df_m_papa[ P039_01 == "Comunidad nativa", sum(P025)]
# Uniendo otras bases de datos de interes
df_m_papa <- merge(df_m_papa,
data_list[[7]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN"),
all.x = TRUE)
# UA que usan semilla y/o plantones certificados
nrow(df_m_papa[P051 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican guano, estiercol u abono organico
nrow(df_m_papa[P052 == "En cantidad suficiente" | P052 == "En poca cantidad",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican fertilizantes quimicos
nrow(df_m_papa[P053 == "En cantidad suficiente" | P053 == "En poca cantidad",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican insecticidas quimicos
nrow(df_m_papa[P054_01 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican insecticidas no quimicos o biologico
nrow(df_m_papa[P054_02 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican herbicida
nrow(df_m_papa[P054_03 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican fungicida
nrow(df_m_papa[P054_04 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican control biologico
nrow(df_m_papa[P056 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Uniendo otras bases de datos de interes
data_list[[10]] <- data_list[[10]][data_list[[10]]$P110 == "Productor(a)",]
df_m_papa <- merge(df_m_papa,
data_list[[10]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN"),
all.x = TRUE)
# UA segun sexo del productor
nrow(df_m_papa[P111 == "Hombre",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P111 == "Mujer",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Edad promedio del productor
df_m_papa[ , mean(P112, na.rm = T)]
# UA segun nivel de educacion del productor
nrow(df_m_papa[P114 == "Sin nivel",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P114 == "Inicial",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P114 == "Primaria incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P114 == "Primaria completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P114 == "Secundaria incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P114 == "Secundaria completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P114 == "Superior no univ. incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P114 == "Superior no univ. completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P114 == "Superior univ. incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P114 == "Superior univ. completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA segun lengua de origen del productor
nrow(df_m_papa[P115 == "Quechua",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P115 == "Aymara",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P115 == "Ashaninka",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P115 == "Otra lengua nativa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P115 == "Castellano",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P115 == "Idioma extranjero",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Uniendo otras bases de datos de interes
df_m_papa <- df_m_papa[,-c("TIPO_REC.x", "TIPO_REC.y")]
df_m_papa <- merge(df_m_papa,
data_list[[11]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN"),
all.x = TRUE)
# UA segun el tipo de saneamiento y alcantarillado
nrow(df_m_papa[P118 == "Red pública de desagüe dentro de la vivienda",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P118 == "Red pública de desagüe, fuera de la vivienda pero dentro de la edificación",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P118 == "Pozo séptico",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P118 == "Pozo ciego o negro / letrina",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P118 == "Río, acequia o canal",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P118 == "Otro",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P118 == "No tiene",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que cuentan con cocina mejorada
nrow(df_m_papa[P119 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que cuentan con computadora
nrow(df_m_papa[P120_01 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que cuentan con internet
nrow(df_m_papa[P120_02 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA segun lejania o cercania a la capital distrital
nrow(df_m_papa[P121_01 == "Vive en la capital distrital",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_m_papa[P121_01 == "Más de 24 horas",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Guardar base de datos final agregando la base de datos de referencia espacial
df_m_papa <- merge(df_m_papa,
data_list[[1]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN"),
all.x = TRUE)
write.csv(df_m_papa, "data-m-cenagro.csv", row.names = F)
###############################################################
# 3.- UNIDADES AGROPECUARIAS QUE SIEMBRAN SOLO NATIVA
# Seleccionar solo las parcelas que contengan papa nativa
df_n_papa <- df_nym_total_2[P024_03 %in% c(2610, 2611, 2613, 2614, 2615) ]
# Número de parcelas por unidad agropecuaria que solo tienen papa nativa
df_parcelas_n_papa <- df_n_papa[ , max(NPARCX) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)]
# Numero de unidades agropecuarias que siembran mejorada
nrow(df_parcelas_n_papa)
# Superficie sembrada del cultivo en hectareas
sum(df_n_papa$P025)
# Superficie que se encuentra bajo riego y secano
df_n_papa[ P026 == "Riego", sum(P025)]
df_n_papa[ P026 == "Secano", sum(P025)]
# Superficie segun destino de la produccion
df_n_papa[ P028 == "Autoconsumo", sum(P025)]
df_n_papa[ P028 == "Autoinsumo", sum(P025)]
df_n_papa[ P028 == "Alimento para sus animales", sum(P025)]
df_n_papa[ P028 == "Venta", sum(P025)]
# Superficie segun destino de venta
df_n_papa[ P029_01 == "Mercado nacional", sum(P025)]
df_n_papa[ P029_02 == "Mercado exterior", sum(P025)]
df_n_papa[ P029_03 == "Agroindustria", sum(P025)]
# Uniendo otras bases de datos de interes
df_n_papa <- merge(df_n_papa,
data_list[[4]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN", "NPARCX"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN", "NPARCY"),
all.x = TRUE)
# UA segun regimen de pertenencia de las parcelas
nrow(df_n_papa[P037_01_03 == "Con título inscrito en registros públicos",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P037_01_03 == "Con título no inscrito en registros públicos",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P037_01_03 == "Sin título, pero en trámite de título",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P037_01_03 == "Sin título, ni trámite",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA segun pertenencia a una comunidad campesina o nativa
nrow(df_n_papa[P039_01 == "Comunidad campesina",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P039_01 == "Comunidad nativa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Superficie sembrada del cultivo segun pertenencia a una comunidad
df_n_papa[ P039_01 == "Comunidad campesina", sum(P025)]
df_n_papa[ P039_01 == "Comunidad nativa", sum(P025)]
# Uniendo otras bases de datos de interes
df_n_papa <- merge(df_n_papa,
data_list[[7]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN"),
all.x = TRUE)
# UA que usan semilla y/o plantones certificados
nrow(df_n_papa[P051 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican guano, estiercol u abono organico
nrow(df_n_papa[P052 == "En cantidad suficiente" | P052 == "En poca cantidad",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican fertilizantes quimicos
nrow(df_n_papa[P053 == "En cantidad suficiente" | P053 == "En poca cantidad",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican insecticidas quimicos
nrow(df_n_papa[P054_01 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican insecticidas no quimicos o biologico
nrow(df_n_papa[P054_02 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican herbicida
nrow(df_n_papa[P054_03 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican fungicida
nrow(df_n_papa[P054_04 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA que aplican control biologico
nrow(df_n_papa[P056 == "Si",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Uniendo otras bases de datos de interes
data_list[[10]] <- data_list[[10]][data_list[[10]]$P110 == "Productor(a)",]
df_n_papa <- merge(df_n_papa,
data_list[[10]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN"),
all.x = TRUE)
# UA segun sexo del productor
nrow(df_n_papa[P111 == "Hombre",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P111 == "Mujer",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Edad promedio del productor
df_n_papa[ , mean(P112, na.rm = T)]
# UA segun nivel de educacion del productor
nrow(df_n_papa[P114 == "Sin nivel",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P114 == "Inicial",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P114 == "Primaria incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P114 == "Primaria completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P114 == "Secundaria incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P114 == "Secundaria completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P114 == "Superior no univ. incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P114 == "Superior no univ. completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P114 == "Superior univ. incompleta",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P114 == "Superior univ. completa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# UA segun lengua de origen del productor
nrow(df_n_papa[P115 == "Quechua",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P115 == "Aymara",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P115 == "Ashaninka",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P115 == "Otra lengua nativa",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P115 == "Castellano",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P115 == "Idioma extranjero",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
# Uniendo otras bases de datos de interes
df_n_papa <- df_n_papa[,-c("TIPO_REC.x", "TIPO_REC.y")]
df_n_papa <- merge(df_n_papa,
data_list[[11]],
by.x = c("P001","P002","P003","P007X","P008", "NPRIN"),
by.y = c("P001","P002","P003","P007X","P008", "NPRIN"),
all.x = TRUE)
# UA segun el tipo de saneamiento y alcantarillado
nrow(df_n_papa[P118 == "Red pública de desagüe dentro de la vivienda",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P118 == "Red pública de desagüe, fuera de la vivienda pero dentro de la edificación",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P118 == "Pozo séptico",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P118 == "Pozo ciego o negro / letrina",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])
nrow(df_n_papa[P118 == "Río, acequia o canal",
length(unique(NPARCX)) ,
by = list(P001, P002, P003, P007X, P008, NPRIN)])